This paper studies the eeect of array calibration errors on the performance of various DF (direction nding) based signal copy algorithms. Unlike blind copy methods, this class of algorithms requires an estimate of the directions of arrival (DOAs) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is suuciently long, the following algorithm...

We derive optimal estimators of a linear combination of the responses, under the random permutation model, incorporating auxiliary information and response error. In particular, we propose estimators of a measure of association, that is named B and defined farther in (1), between a response and an explanatory variable based on a simple random sample obtained without replacement from a finite po...

Consider the problem of estimating a function f known to lie in a convex, compact subset F of L2[0, 1], when one observes data on f containing white Gaussian noise. We establish an upper bound on the integrated mean-squared error of least-squares estimates which uses the asymptotic properties of the Gel'fand n-widths of F. The bound shows that if the Gel'fand n-widths tend to zero at a faster r...

An attractive alternative to least-squares data determined by using the median rather than the modeling techniques is the use of absolute value arithmetic mean. Algorithms for absolute error error criteria. Unlike the least-squares techniques minimization are often approximately as costly the inclusion of some infinite blunders along with as least-squares algorithms; however, unlike the data wi...

Inter-symbol interference if not taken care off may cause severe error at the receiver and the detection of signal becomes difficult. An adaptive equalizer employing Recursive Least Squares algorithm can be a good compensation for the ISI problem. In this paper performance of communication link in presence of Least Mean Square and Recursive Least Squares equalizer algorithm is analyzed. A Model...

This work incorporates the identification of model in functional form using curve fitting and genetic programming technique which can forecast present and future load requirement. Approximating an unknown function with sample data is an important practical problem. In order to forecast an unknown function using a finite set of sample data, a function is constructed to fit sample data points. Th...

This paper presents an efficient and general solution to the linear regression problem using the Minimum Message Length (MML) principle. Inference in an MML framework involves optimising a two-part costing function that describes the trade-off between model complexity and model capability. The MML criterion is integrated into the orthogonal least squares algorithm (MML-OLS) to improve both spee...